Machine Learning-based System Development Process Model

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A Machine Learning-based System Development Process Model is a software development process model to develop machine learning-based systems.



References

2019b

2017

2016

  • http://databricks.com/blog/2016/05/31/apache-spark-2-0-preview-machine-learning-model-persistence.html
    • QUOTE: So far, we have only looked at saving and loading a single ML model. In practice, ML workflows consist of many stages, from feature extraction and transformation to model fitting and tuning. MLlib provides Pipelines to help users construct these workflows. (See this notebook for a tutorial on ML Pipelines analyzing a bike sharing dataset.)

       MLlib allows users to save and load entire Pipelines. Let’s look at how this is done on an example Pipeline with these steps:

      • Feature extraction: Binarizer to convert images to black and white
      • Model fitting: Random Forest Classifier to take images and predict digits 0–9
      • Tuning: Cross-Validation to tune the depth of the trees in the forest